src/streamlit_app.py

import streamlit as st
import pandas as pd
import numpy as np
import faiss
import os

from sentence_transformers import SentenceTransformer
from huggingface_hub import InferenceClient

# ==============================
# CONFIG
# ==============================
st.set_page_config(page_title="Company ChatGPT", layout="wide")
st.title("🏢 Company AI Assistant (RAG Powered)")

# ==============================
# LOAD MODELS
# ==============================
@st.cache_resource
def load_models():
    embed_model = SentenceTransformer("all-MiniLM-L6-v2")

    HF_TOKEN = os.environ.get("HF_TOKEN")
    if not HF_TOKEN:
        st.error("❌ Please add HF_TOKEN in Hugging Face Secrets")
        st.stop()

    llm = InferenceClient(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        token=HF_TOKEN
    )
    return embed_model, llm

embed_model, llm = load_models()

# ==============================
# LOAD DATA
# ==============================
@st.cache_data
def load_data():
    path = "src/company_sample.csv"
    if not os.path.exists(path):
        st.error(f"❌ File not found: {path}")
        st.stop()
    df = pd.read_csv(path)
    return df

df = load_data()

if "text" not in df.columns:
    st.error("❌ CSV must contain 'text' column")
    st.stop()

documents = df["text"].fillna("").tolist()

# ==============================
# CREATE VECTOR DB
# ==============================
@st.cache_resource
def create_faiss(docs):
    embeddings = embed_model.encode(docs)
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(np.array(embeddings))
    return index

index = create_faiss(documents)

# ==============================
# RETRIEVAL FUNCTION
# ==============================
def retrieve(query, top_k=3):
    q_emb = embed_model.encode([query])
    D, I = index.search(np.array(q_emb), top_k)
    return [documents[i] for i in I[0] if i < len(documents)]

# ==============================
# CHAT HISTORY
# ==============================
if "messages" not in st.session_state:
    st.session_state.messages = []

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

# ==============================
# USER INPUT
# ==============================
query = st.chat_input("Ask about company...")

if query:
    st.session_state.messages.append({"role": "user", "content": query})
    st.chat_message("user").write(query)

    # 🔍 Retrieve context
    context_docs = retrieve(query)
    context = "\n\n".join(context_docs)

    # ==============================
    # 🤖 LLM CALL (FIXED)
    # ==============================
    try:
        response = llm.chat_completion(
            messages=[
                {
                    "role": "system",
                    "content": "You are a company assistant. Answer ONLY from given context. If not found, say 'Not available in company data.'"
                },
                {
                    "role": "user",
                    "content": f"""
Context:
{context}

Question:
{query}
"""
                }
            ],
            max_tokens=200,
            temperature=0.5
        )

        answer = response.choices[0].message.content

    except Exception as e:
        answer = f"❌ Error: {str(e)}"

    # ==============================
    # DISPLAY RESPONSE
    # ==============================
    st.session_state.messages.append({"role": "assistant", "content": answer})
    st.chat_message("assistant").write(answer)